class_docstr: Construct an MBART tokenizer.
  
  Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
  [SentencePiece](https://github.com/google/sentencepiece).
  
  The tokenization method is `<tokens> <eos> <language code>` for source language documents, and `<language code>
  <tokens> <eos>` for target language documents.
  
  Examples:
  
  ```python
  >>> from transformers import MBartTokenizer
  
  >>> tokenizer = MBartTokenizer.from_pretrained("facebook/mbart-large-en-ro", src_lang="en_XX", tgt_lang="ro_RO")
  >>> example_english_phrase = " UN Chief Says There Is No Military Solution in Syria"
  >>> expected_translation_romanian = "Şeful ONU declară că nu există o soluţie militară în Siria"
  >>> inputs = tokenizer(example_english_phrase, text_target=expected_translation_romanian, return_tensors="pt")
  ```
docstr: Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code].
docstr_tok: ["Reset","the","special","tokens","to","the","source","lang","setting",".","No","prefix","and","suffix=","[","eos",",","src_lang_code","]","."]
code: def set_src_lang_special_tokens(self, src_lang) -> None:
      """Reset the special tokens to the source lang setting. No prefix and suffix=[eos, src_lang_code]."""
      self.cur_lang_code = self.lang_code_to_id[src_lang]
      self.prefix_tokens = []
      self.suffix_tokens = [self.eos_token_id, self.cur_lang_code]
  
imports: import os
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple
  import sentencepiece
  from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
  from ...utils import logging
label_desc: Use image node_id 20 for calling the MBartTokenizer obj's underlying member method code with example usage: obj.set_src_lang_special_tokens(src_lang) without return types